{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Scatter Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
    "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
    "We’ll start by setting up the notebook for plotting and importing the functions we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.plot``\n",
    "\n",
    "In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n",
    "It turns out that this same function can produce scatter plots as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FRUUAgKysLAwNDcHtdgMAHA4HUlJSMHPmTMTExKCgoABWq3XcNpMxOvdrt9vh9Xp9c79M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\ndyIiGWJyJyKSof8HrbC+n6hPVeEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec0b1dfe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, 'o', color='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HvxBCLMJp4gR4b9mCguXLUf7bb6jatx/eW7bAaWLPT84BhNl6t7q6GpGRkdi/fz80Gg2e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\nUzV97i0FigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec08e6f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
    "    plt.plot(rng.rand(5), rng.rand(5), marker,\n",
    "             label=\"marker='{0}'\".format(marker))\n",
    "plt.legend(numpoints=1)\n",
    "plt.xlim(0, 1.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NYoZN3iX3lJQUvPnmm7QQsA54e3vj9u3bBld7NBapqakQCoUYO3Ys26Hwikwm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec0b1dfd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-ok');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H5lxuMbUCzE1DWDLvx0XjfEcLCQkxew3Nzc3cDIa4nFgX+C0hk8nM/hi5w6Kn22TkEokE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\n/w96X17OMnwkqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec08ce0668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-p', color='gray',\n",
    "         markersize=15, linewidth=4,\n",
    "         markerfacecolor='white',\n",
    "         markeredgecolor='gray',\n",
    "         markeredgewidth=2)\n",
    "plt.ylim(-1.2, 1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n",
    "For a full description of the options available, refer to the ``plt.plot`` documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.scatter``\n",
    "\n",
    "A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9/8c//oEf/ehHAB4+wc4bJB3cbTaby5dACU95CgwMxFNPPQUAOHLkCNLT0xEY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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec08ce64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, marker='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
    "\n",
    "Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
    "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sr0Nui/jQOEhoV7uoROFkbbJlH7HFkl1pjRQS33yyxqttmeR8lzBKcPbo2Doo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec08c052b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100)\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,cmap='viridis')\n",
    "plt.colorbar();  # show color scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n",
    "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
    "\n",
    "For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3/+uNdOxaZ4efS7P/zj1ROKnus/vuHbR259b1GNcrWScGRFPIVrvS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec36415048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "features = iris.data.T\n",
    "\n",
    "plt.scatter(features[0], features[1], alpha=0.2,\n",
    "            s=100*features[3], c=iris.target, cmap='viridis')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
    "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
    "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plot`` Versus ``scatter``: A Note on Efficiency\n",
    "\n",
    "Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n",
    "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
    "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
    "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Simple Line Plots](04.01-Simple-Line-Plots.ipynb) | [Contents](Index.ipynb) | [Visualizing Errors](04.03-Errorbars.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
